CCIPS: A Cooperative Intrusion Detection and Prevention Framework for Cloud Services
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the recent emergence and rapid advancement of cloud computing infrastructure and services, outsourcing Information Technology (IT) and digital services to Cloud Providers (CPs) has become attractive. This will allow for a reduction in IT resources (hardware, software, services, support, and staffing), and provide flexibility and agility in resource allocation, data and resource delivery, fault-tolerance, and scalability. However, the majority of cloud service providers tailor their services to address functionality (such as availability, speed, and utilization) and design requirements (such as integration), rather than protection against cyber-attacks and associated security issues. This paper considers the detection and prevention of security attacks against cloud computing systems. A proactive Cooperative Cloud Intrusion Prevention System (CCIPS) framework is proposed to detect and prevent known and zero-day threats targeting cloud computing networks. This framework provides enhanced threat detection and prevention via behavioral and anomaly data analysis. A multi-layer approach to security is employed to provide a cooperative model cloud which has both high performance and high availability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it